Aiming　at　the　challenging　problems　of　the　deficient　accuracy　and　generalization　ability　of　the　furnace　temperature　prediction　model　when　the　municipal　solid　waste　(MSW)　incineration　process　data　has　abnormal　values　and　high　dimensionality　of　feature　variables,　a　robust　weighted　heterogeneous　feature　ensemble　modeling　method　is　proposed　to　establish　the　furnace　temperature　prediction　model　of　the　municipal　solid　waste　incineration　process.　Firstly,　the　high　dimensional　feature　variables　are　divided　into　heterogeneous　feature　sets　according　to　the　incineration　process　mechanism,　and　the　contribution　of　each　heterogeneous　feature　set　is　evaluated　by　the　mutual　information　and　correlation　coefficient.　Secondly,　a　robust　stochastic　configuration　network　(SCN)　with　the　t　mixture　distribution　is　employed　to　construct　base　models,　and　penalty　weights　of　training　samples　are　determined　at　the　same　time.　Finally,　the　robust　weighted　negative　correlation　learning　(NCL)　strategy　is　used　to　realize　the　synchronous　training　of　base　models.　Comparative　experiments　are　carried　out　using　the　historical　furnace　temperature　data　of　a　municipal　solid　waste　incineration　plant　in　China.　The　results　show　that　the　furnace　temperature　prediction　model　established　by　the　proposed　method　performs　more　favourably　in　accuracy　and　generalization.　©　2024　Science　Press.　All　rights　reserved.